Update documentation for Distributed Training TensorFlow Cloud tutorial

PiperOrigin-RevId: 375808742

Author: g-luo

g3doc/tutorials/distributed_training_nasnet_with_tensorflow_cloud.ipynb

@@ -82,7 +82,8 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "Kw7YV8nN0gEb"
+        "id": "Kw7YV8nN0gEb",
+        "outputId": "7eed43ea-d108-4aa0-90b9-f52e7d7d8eab"
       },
       "outputs": [
         {
@@ -110,7 +111,8 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "kA1D8jB3TviQ"
+        "id": "kA1D8jB3TviQ",
+        "outputId": "27c3a25f-ee92-47a1-edab-5e087e749d14"
       },
       "outputs": [
         {
@@ -136,7 +138,8 @@
       },
       "outputs": [],
       "source": [
-        "import sys"
+        "import sys\n",
+        "import os"
       ]
     },
     {
@@ -307,12 +310,12 @@
         "    # Freeze the pretrained weights\n",
         "    model.trainable = False\n",
         "\n",
-        "    # We unfreeze the top 20 layers while leaving BatchNorm layers frozen\n",
+        "    # Unfreeze the top 20 layers while leaving BatchNorm layers frozen\n",
         "    for layer in model.layers[-20:]:\n",
         "        if not isinstance(layer, layers.BatchNormalization):\n",
         "            layer.trainable = True\n",
         "\n",
-        "    # Rebuild top\n",
+        "    # Rebuild non-frozen top layers of NASNetMobile, which was initialized with include_top=False\n",
         "    x = layers.GlobalAveragePooling2D(name=\"avg_pool\")(model.output)\n",
         "    x = layers.BatchNormalization()(x)\n",
         "\n",
@@ -341,6 +344,7 @@
       "source": [
         "model = build_model(NUM_CLASSES, INPUT_IMG_SIZE)\n",
         "\n",
+        "# Set up job on TensorFlow Cloud to train for 100 epochs on all the data\n",
         "if tfc.remote():\n",
         "    # Configure Tensorboard logs\n",
         "    callbacks=[\n",
@@ -359,7 +363,7 @@
         "    model.save(SAVED_MODEL_DIR)\n",
         "\n",
         "else:\n",
-        "    # Run the training for 1 epoch and a small subset of the data to validate setup\n",
+        "    # Run the training locally for 1 epoch and a small subset of the data to validate setup\n",
         "    model.fit(x=x_train[:100], y=y_train[:100], validation_split=0.2, epochs=1)"
       ]
     },
@@ -382,8 +386,7 @@
       },
       "outputs": [],
       "source": [
-        "# If you are using a custom image you can install modules via requirements\n",
-        "# txt file.\n",
+        "# You can install custom modules for your Docker images via requirements txt file.\n",
         "with open('requirements.txt','w') as f:\n",
         "    f.write('tensorflow-cloud\\n')\n",
         "\n",
@@ -465,6 +468,7 @@
         "cIG5d4Kvls6m"
       ],
       "name": "distributed_training_nasnet_with_tensorflow_cloud.ipynb",
+      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

g3doc/tutorials/hp_tuning_cifar10_using_google_cloud.ipynb

@@ -83,7 +83,8 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "a751fAMsyu0q"
+        "id": "a751fAMsyu0q",
+        "outputId": "eb50e5e8-51ff-484f-c863-fba3ca7f6f49"
       },
       "outputs": [
         {
@@ -117,7 +118,8 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "BnUf9-AA_pZ_"
+        "id": "BnUf9-AA_pZ_",
+        "outputId": "1bb6e925-0853-443c-9b8c-9ae90bae8b69"
       },
       "outputs": [
         {
@@ -406,7 +408,7 @@
       },
       "outputs": [],
       "source": [
-        "# If you are using a custom image you can install modules via requirements txt file.\n",
+        "# You can install custom modules for your Docker images via requirements txt file.\n",
         "with open('requirements.txt','w') as f:\n",
         "    f.write('pandas==1.1.5\\n')\n",
         "    f.write('numpy==1.18.5\\n')\n",
@@ -496,6 +498,7 @@
         "2ej8rnJkmoA8"
       ],
       "name": "hp_tuning_cifar10_using_google_cloud.ipynb",
+      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {